Code
./data/20240428T200156-politicians-aux-analysis/n0001-init//n0001-models-phase-i0021-all/fit0x0.extension
Set Working Directory
Imports
Input and Output Directories and Files
Check Period and Phase in df2
Check Period and Phase in df3
Prepare for Regression
./data/20240428T200156-politicians-aux-analysis/n0001-init//n0001-models-phase-i0021-all/fit0x0.extension
Fitting and Marginalization
Cleanup
Save Data for Reference
Source Helpers
fit01aPh: Nullfit01aPh: [df0] Agency ~ (1 | Name) + 1
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Agency ~ (1 | Name) + 1
Data: df0
Control: control
REML criterion at convergence: 26631.7
Scaled residuals:
Min 1Q Median 3Q Max
-7.9514 -0.5639 -0.0023 0.5683 7.3986
Random effects:
Groups Name Variance Std.Dev.
Name (Intercept) 0.006209 0.0788
Residual 0.067519 0.2598
Number of obs: 169997, groups: Name, 870
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.986e-01 2.807e-03 8.264e+02 177.7 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit01aPh: [df0] Agency ~ (1 | Name) + 1
# R2 for Mixed Models
Conditional R2: 0.084
Marginal R2: 0.000
---------------------------------------------------------------------
fit01aPh: [df0] Agency ~ (1 | Name) + 1
# Intraclass Correlation Coefficient
Adjusted ICC: 0.084
Unadjusted ICC: 0.084
---------------------------------------------------------------------
fit01aPh: [df0] Agency ~ (1 | Name) + 1
# ICC by Group
Group | ICC
-------------
Name | 0.084
---------------------------------------------------------------------
model <- "fit01aPh"
extra <- "9001"
terms <- NULL
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- sjPlot::plot_model(get(model), type="re")
fbasefig <- file.path(ofd4, paste0(model, "-xtr-", extra, "-random", paste(terms, collapse = "-x-")))
ggsave(file=paste0(fbasefig,".png"),plot=gg88,width=8,height=88,limitsize=FALSE)
ggsave(file=paste0(fbasefig,".svg"),plot=gg88,width=8,height=88,limitsize=FALSE)
## knitr::opts_chunk$set(fig.width=unit(8,"cm"), fig.height=unit(88,"cm"))
gg88fit02aPh: Timefit02aPh: [df0] Agency ~ (Time | Name) + Time
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Agency ~ (Time | Name) + Time
Data: df0
Control: control
REML criterion at convergence: 24941.9
Scaled residuals:
Min 1Q Median 3Q Max
-8.0353 -0.5639 -0.0047 0.5664 7.3362
Random effects:
Groups Name Variance Std.Dev. Corr
Name (Intercept) 0.006283 0.07927
Time 0.004088 0.06394 0.18
Residual 0.066384 0.25765
Number of obs: 169997, groups: Name, 870
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.498029 0.002848 816.443563 174.884 < 2e-16 ***
Time -0.010230 0.002634 709.116999 -3.884 0.000112 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit02aPh: [df0] Agency ~ (Time | Name) + Time
# R2 for Mixed Models
Conditional R2: 0.102
Marginal R2: 0.000
---------------------------------------------------------------------
fit02aPh: [df0] Agency ~ (Time | Name) + Time
# Intraclass Correlation Coefficient
Adjusted ICC: 0.102
Unadjusted ICC: 0.102
---------------------------------------------------------------------
fit02aPh: [df0] Agency ~ (Time | Name) + Time
Model contains random slopes. Cannot compute accurate ICCs by group
factors.
# ICC by Group
Group | ICC
-------------
Name | 0.085
---------------------------------------------------------------------
fit02aPh: [df0] Agency ~ (Time | Name) + Time
=====================================================================
# Average predicted values of Agency
Time | Predicted | 95% CI
------------------------------
-1.00 | 0.51 | 0.50, 0.51
-0.50 | 0.50 | 0.50, 0.51
0.00 | 0.50 | 0.49, 0.50
0.50 | 0.50 | 0.49, 0.50
1.00 | 0.49 | 0.48, 0.50
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
--------------------------------
-7.57e-03 | -0.01, 0.00 | 0.004
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
--------------------------------
-7.57e-03 | -0.01, 0.00 | 0.004
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + timeD + lineE + lineT + lineR + rect5 + cogsys::theme0 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit03aPh: Time x Phasefit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Agency ~ (Time | Name) + Time * Phase
Data: df0
Control: control
REML criterion at convergence: 24043.7
Scaled residuals:
Min 1Q Median 3Q Max
-8.1156 -0.5629 -0.0072 0.5649 7.3761
Random effects:
Groups Name Variance Std.Dev. Corr
Name (Intercept) 0.006224 0.07889
Time 0.004129 0.06425 0.18
Residual 0.066017 0.25694
Number of obs: 169997, groups: Name, 870
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.285e-01 3.170e-03 1.284e+03 166.749 <2e-16 ***
Time 4.459e-02 3.657e-03 2.769e+03 12.192 <2e-16 ***
PhaseAE -4.145e-03 4.178e-03 1.691e+05 -0.992 0.3212
PhaseBR -5.689e-01 3.066e-02 1.686e+05 -18.558 <2e-16 ***
PhaseAR -9.121e-03 4.936e-03 1.697e+05 -1.848 0.0646 .
Time:PhaseAE -3.579e-01 2.590e-02 1.688e+05 -13.820 <2e-16 ***
Time:PhaseBR 1.610e+00 9.670e-02 1.686e+05 16.650 <2e-16 ***
Time:PhaseAR -9.963e-02 7.213e-03 1.676e+05 -13.812 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
# R2 for Mixed Models
Conditional R2: 0.107
Marginal R2: 0.006
---------------------------------------------------------------------
fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
# Intraclass Correlation Coefficient
Adjusted ICC: 0.102
Unadjusted ICC: 0.101
---------------------------------------------------------------------
fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
Model contains random slopes. Cannot compute accurate ICCs by group
factors.
# ICC by Group
Group | ICC
-------------
Name | 0.085
---------------------------------------------------------------------
fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
=====================================================================
# Average predicted values of Agency
Time | Predicted | 95% CI
------------------------------
-1.00 | 0.45 | 0.44, 0.47
-0.50 | 0.48 | 0.47, 0.49
0.00 | 0.50 | 0.50, 0.51
0.50 | 0.53 | 0.52, 0.54
1.00 | 0.56 | 0.54, 0.57
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
---------------------------
0.05 | 0.04, 0.06 | < .001
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
---------------------------
0.05 | 0.04, 0.06 | < .001
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
=====================================================================
# Average predicted values of Agency
Phase | Predicted | 95% CI
--------------------------------
BE | 0.53 | 0.52, 0.53
AE | 0.55 | 0.54, 0.56
BR | -0.15 | -0.22, -0.08
AR | 0.53 | 0.51, 0.54
=====================================================================
Phase | Predicted | 95% CI | p
-----------------------------------------
BE | 0.53 | 0.52, 0.53 | < .001
AE | 0.55 | 0.54, 0.56 | < .001
BR | -0.15 | -0.22, -0.08 | < .001
AR | 0.53 | 0.51, 0.54 | < .001
=====================================================================
# Pairwise comparisons
Phase | Contrast | 95% CI | p
----------------------------------------
BE-AE | -0.02 | -0.03, -0.01 | < .001
BE-BR | 0.68 | 0.61, 0.75 | < .001
BE-AR | 2.34e-03 | -0.01, 0.01 | 0.658
AE-BR | 0.70 | 0.63, 0.77 | < .001
AE-AR | 0.02 | 0.01, 0.04 | 0.003
BR-AR | -0.68 | -0.75, -0.60 | < .001
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect4 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit03aPh: [df0] Agency ~ (Time | Name) + Time * Phase
=====================================================================
# Average predicted values of Agency
Phase: BE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.48 | 0.47, 0.49
-0.50 | 0.51 | 0.50, 0.51
0.00 | 0.53 | 0.52, 0.54
0.50 | 0.56 | 0.55, 0.56
1.00 | 0.58 | 0.57, 0.59
Phase: AE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.84 | 0.78, 0.89
-0.50 | 0.68 | 0.65, 0.71
0.00 | 0.53 | 0.52, 0.54
0.50 | 0.37 | 0.35, 0.39
1.00 | 0.22 | 0.17, 0.26
Phase: BR
Time | Predicted | 95% CI
--------------------------------
-1.00 | -1.70 | -1.95, -1.45
-0.50 | -0.87 | -1.02, -0.71
0.00 | -0.04 | -0.10, 0.02
0.50 | 0.79 | 0.76, 0.83
1.00 | 1.62 | 1.49, 1.75
Phase: AR
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.57 | 0.55, 0.60
-0.50 | 0.55 | 0.53, 0.56
0.00 | 0.52 | 0.51, 0.53
0.50 | 0.50 | 0.49, 0.50
1.00 | 0.47 | 0.46, 0.48
=====================================================================
# (Average) Linear trend for Time
Phase | Slope | 95% CI | p
-------------------------------------
BE | 0.04 | 0.04, 0.05 | < .001
AE | -0.31 | -0.36, -0.26 | < .001
BR | 1.65 | 1.47, 1.84 | < .001
AR | -0.06 | -0.07, -0.04 | < .001
=====================================================================
# (Average) Linear trend for Time
Phase | Contrast | 95% CI | p
----------------------------------------
BE-AE | 0.36 | 0.31, 0.41 | < .001
BE-BR | -1.61 | -1.80, -1.42 | < .001
BE-AR | 0.10 | 0.09, 0.11 | < .001
AE-BR | -1.97 | -2.16, -1.77 | < .001
AE-AR | -0.26 | -0.31, -0.21 | < .001
BR-AR | 1.71 | 1.52, 1.90 | < .001
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
fit04aPh: Time x Phase x Outcomefit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Agency ~ (Time | Name) + Time * Phase * Outcome
Data: df0
Control: control
REML criterion at convergence: 23540
Scaled residuals:
Min 1Q Median 3Q Max
-8.1902 -0.5632 -0.0074 0.5639 7.3864
Random effects:
Groups Name Variance Std.Dev. Corr
Name (Intercept) 0.004798 0.06927
Time 0.003348 0.05786 -0.07
Residual 0.065916 0.25674
Number of obs: 169997, groups: Name, 870
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.151e-01 4.450e-03 1.471e+03 115.767 < 2e-16
Time 5.703e-02 5.597e-03 3.426e+03 10.189 < 2e-16
PhaseAE -4.245e-02 7.688e-03 1.693e+05 -5.521 3.38e-08
PhaseBR -4.966e-01 6.025e-02 1.690e+05 -8.242 < 2e-16
PhaseAR -1.195e-01 1.034e-02 1.662e+05 -11.561 < 2e-16
Outcomewinner 2.383e-02 5.853e-03 1.425e+03 4.072 4.92e-05
Time:PhaseAE -4.906e-01 5.053e-02 1.692e+05 -9.710 < 2e-16
Time:PhaseBR 1.152e+00 1.903e-01 1.689e+05 6.052 1.44e-09
Time:PhaseAR -5.823e-02 1.523e-02 1.543e+05 -3.824 0.000131
Time:Outcomewinner -2.039e-02 7.198e-03 3.276e+03 -2.833 0.004646
PhaseAE:Outcomewinner 5.796e-02 9.165e-03 1.692e+05 6.324 2.56e-10
PhaseBR:Outcomewinner -9.406e-02 6.997e-02 1.689e+05 -1.344 0.178864
PhaseAR:Outcomewinner 1.478e-01 1.178e-02 1.679e+05 12.550 < 2e-16
Time:PhaseAE:Outcomewinner 1.675e-01 5.885e-02 1.692e+05 2.846 0.004424
Time:PhaseBR:Outcomewinner 6.256e-01 2.209e-01 1.688e+05 2.832 0.004623
Time:PhaseAR:Outcomewinner -4.478e-02 1.731e-02 1.602e+05 -2.586 0.009706
(Intercept) ***
Time ***
PhaseAE ***
PhaseBR ***
PhaseAR ***
Outcomewinner ***
Time:PhaseAE ***
Time:PhaseBR ***
Time:PhaseAR ***
Time:Outcomewinner **
PhaseAE:Outcomewinner ***
PhaseBR:Outcomewinner
PhaseAR:Outcomewinner ***
Time:PhaseAE:Outcomewinner **
Time:PhaseBR:Outcomewinner **
Time:PhaseAR:Outcomewinner **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
# R2 for Mixed Models
Conditional R2: 0.102
Marginal R2: 0.021
---------------------------------------------------------------------
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
# Intraclass Correlation Coefficient
Adjusted ICC: 0.083
Unadjusted ICC: 0.081
---------------------------------------------------------------------
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
Model contains random slopes. Cannot compute accurate ICCs by group
factors.
# ICC by Group
Group | ICC
-------------
Name | 0.067
---------------------------------------------------------------------
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
# Fixed Effects
Parameter | Coefficient | SE | 95% CI | t(169977) | p
-----------------------------------------------------------------------------------------------------
(Intercept) | 0.52 | 4.45e-03 | [ 0.51, 0.52] | 115.77 | < .001
Time | 0.06 | 5.60e-03 | [ 0.05, 0.07] | 10.19 | < .001
Phase [AE] | -0.04 | 7.69e-03 | [-0.06, -0.03] | -5.52 | < .001
Phase [BR] | -0.50 | 0.06 | [-0.61, -0.38] | -8.24 | < .001
Phase [AR] | -0.12 | 0.01 | [-0.14, -0.10] | -11.56 | < .001
Outcome [winner] | 0.02 | 5.85e-03 | [ 0.01, 0.04] | 4.07 | < .001
Time × Phase [AE] | -0.49 | 0.05 | [-0.59, -0.39] | -9.71 | < .001
Time × Phase [BR] | 1.15 | 0.19 | [ 0.78, 1.52] | 6.05 | < .001
Time × Phase [AR] | -0.06 | 0.02 | [-0.09, -0.03] | -3.82 | < .001
Time × Outcome [winner] | -0.02 | 7.20e-03 | [-0.03, -0.01] | -2.83 | 0.005
Phase [AE] × Outcome [winner] | 0.06 | 9.17e-03 | [ 0.04, 0.08] | 6.32 | < .001
Phase [BR] × Outcome [winner] | -0.09 | 0.07 | [-0.23, 0.04] | -1.34 | 0.179
Phase [AR] × Outcome [winner] | 0.15 | 0.01 | [ 0.12, 0.17] | 12.55 | < .001
(Time × Phase [AE]) × Outcome [winner] | 0.17 | 0.06 | [ 0.05, 0.28] | 2.85 | 0.004
(Time × Phase [BR]) × Outcome [winner] | 0.63 | 0.22 | [ 0.19, 1.06] | 2.83 | 0.005
(Time × Phase [AR]) × Outcome [winner] | -0.04 | 0.02 | [-0.08, -0.01] | -2.59 | 0.010
# Random Effects
Parameter | Coefficient
----------------------------------------
SD (Intercept: Name) | 0.07
SD (Time: Name) | 0.06
Cor (Intercept~Time: Name) | -0.07
SD (Residual) | 0.26
Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
using a Wald t-distribution approximation.
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 169997' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 169997)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 169997' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 169997)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 169997' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 169997)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 169997' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 169997)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
| Characteristic | Beta | 95% CI1 | p-value |
|---|---|---|---|
| Time | 0.06 | 0.05, 0.07 | <0.001 |
| Phase | |||
| AE - BE | 0.01 | 0.00, 0.03 | 0.10 |
| BR - BE | -0.64 | -0.75, -0.53 | <0.001 |
| BR - AE | -0.66 | -0.77, -0.55 | <0.001 |
| AR - BE | -0.04 | -0.06, -0.02 | <0.001 |
| AR - AE | -0.05 | -0.08, -0.03 | <0.001 |
| AR - BR | 0.60 | 0.49, 0.71 | <0.001 |
| Outcome | |||
| winner - loser | 0.04 | 0.00, 0.08 | 0.069 |
| Time * Phase | |||
| Time * AE | -0.49 | -0.59, -0.39 | <0.001 |
| Time * BR | 1.2 | 0.78, 1.5 | <0.001 |
| Time * AR | -0.06 | -0.09, -0.03 | <0.001 |
| Time * Outcome | |||
| Time * winner | -0.02 | -0.03, -0.01 | 0.005 |
| Phase * Outcome | |||
| AE * winner | 0.06 | 0.04, 0.08 | <0.001 |
| BR * winner | -0.09 | -0.23, 0.04 | 0.2 |
| AR * winner | 0.15 | 0.12, 0.17 | <0.001 |
| Time * Phase * Outcome | |||
| Time * AE * winner | 0.17 | 0.05, 0.28 | 0.004 |
| Time * BR * winner | 0.63 | 0.19, 1.1 | 0.005 |
| Time * AR * winner | -0.04 | -0.08, -0.01 | 0.010 |
| Name.sd__(Intercept) | 0.07 | ||
| Name.cor__(Intercept).Time | -0.07 | ||
| Name.sd__Time | 0.06 | ||
| Residual.sd__Observation | 0.26 | ||
| 1 CI = Confidence Interval | |||
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
=====================================================================
# Average predicted values of Agency
Time | Predicted | 95% CI
------------------------------
-1.00 | 0.46 | 0.44, 0.47
-0.50 | 0.48 | 0.47, 0.49
0.00 | 0.50 | 0.50, 0.51
0.50 | 0.53 | 0.52, 0.54
1.00 | 0.55 | 0.54, 0.56
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
---------------------------
0.05 | 0.04, 0.06 | < .001
=====================================================================
# (Average) Linear trend for Time
Slope | 95% CI | p
---------------------------
0.05 | 0.04, 0.06 | < .001
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 + timeD + lineE + lineT + lineR + rect3 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
=====================================================================
# Average predicted values of Agency
Phase | Predicted | 95% CI
--------------------------------
BE | 0.52 | 0.52, 0.53
AE | 0.55 | 0.54, 0.57
BR | -0.12 | -0.19, -0.04
AR | 0.51 | 0.50, 0.52
=====================================================================
Phase | Predicted | 95% CI | p
-----------------------------------------
BE | 0.52 | 0.52, 0.53 | < .001
AE | 0.55 | 0.54, 0.57 | < .001
BR | -0.12 | -0.19, -0.04 | 0.004
AR | 0.51 | 0.50, 0.52 | < .001
=====================================================================
# Pairwise comparisons
Phase | Contrast | 95% CI | p
----------------------------------------
BE-AE | -0.03 | -0.04, -0.02 | < .001
BE-BR | 0.64 | 0.56, 0.72 | < .001
BE-AR | 0.02 | 0.00, 0.03 | 0.007
AE-BR | 0.67 | 0.59, 0.75 | < .001
AE-AR | 0.05 | 0.03, 0.06 | < .001
BR-AR | -0.62 | -0.70, -0.55 | < .001
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect4 ## + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
=====================================================================
# Average predicted values of Agency
Phase: BE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.48 | 0.48, 0.49
-0.50 | 0.51 | 0.50, 0.51
0.00 | 0.53 | 0.52, 0.53
0.50 | 0.55 | 0.54, 0.56
1.00 | 0.57 | 0.56, 0.58
Phase: AE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.86 | 0.80, 0.92
-0.50 | 0.69 | 0.66, 0.72
0.00 | 0.53 | 0.52, 0.53
0.50 | 0.36 | 0.34, 0.38
1.00 | 0.19 | 0.15, 0.24
Phase: BR
Time | Predicted | 95% CI
--------------------------------
-1.00 | -1.65 | -1.90, -1.40
-0.50 | -0.84 | -1.00, -0.69
0.00 | -0.03 | -0.09, 0.03
0.50 | 0.78 | 0.74, 0.81
1.00 | 1.59 | 1.46, 1.72
Phase: AR
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.55 | 0.53, 0.58
-0.50 | 0.53 | 0.51, 0.55
0.00 | 0.51 | 0.50, 0.52
0.50 | 0.49 | 0.48, 0.49
1.00 | 0.47 | 0.46, 0.47
=====================================================================
# (Average) Linear trend for Time
Phase | Slope | 95% CI | p
-----------------------------------------
BE | 0.06 | 0.05, 0.07 | < .001
AE | -0.43 | -0.53, -0.33 | < .001
BR | 1.21 | 0.84, 1.58 | < .001
AR | -1.19e-03 | -0.03, 0.03 | 0.936
=====================================================================
# (Average) Linear trend for Time
Phase | Contrast | 95% CI | p
----------------------------------------
BE-AE | 0.49 | 0.39, 0.59 | < .001
BE-BR | -1.15 | -1.52, -0.78 | < .001
BE-AR | 0.06 | 0.03, 0.09 | < .001
AE-BR | -1.64 | -2.03, -1.26 | < .001
AE-AR | -0.43 | -0.53, -0.33 | < .001
BR-AR | 1.21 | 0.84, 1.58 | < .001
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
=====================================================================
# Average predicted values of Agency
Outcome: loser
Phase | Predicted | 95% CI
--------------------------------
BE | 0.51 | 0.50, 0.52
AE | 0.50 | 0.48, 0.52
BR | -0.07 | -0.21, 0.08
AR | 0.39 | 0.37, 0.42
Outcome: winner
Phase | Predicted | 95% CI
--------------------------------
BE | 0.53 | 0.53, 0.54
AE | 0.57 | 0.56, 0.59
BR | -0.18 | -0.26, -0.09
AR | 0.57 | 0.56, 0.58
=====================================================================
Phase | Outcome | Predicted | 95% CI | p
---------------------------------------------------
BE | loser | 0.51 | 0.50, 0.52 | < .001
AE | loser | 0.50 | 0.48, 0.52 | < .001
BR | loser | -0.07 | -0.21, 0.08 | 0.364
AR | loser | 0.39 | 0.37, 0.42 | < .001
BE | winner | 0.53 | 0.53, 0.54 | < .001
AE | winner | 0.57 | 0.56, 0.59 | < .001
BR | winner | -0.18 | -0.26, -0.09 | < .001
AR | winner | 0.57 | 0.56, 0.58 | < .001
=====================================================================
# Pairwise comparisons
Phase | Outcome | Contrast | 95% CI | p
--------------------------------------------------------
BE-AE | loser-loser | 9.06e-03 | -0.01, 0.03 | 0.404
BE-BR | loser-loser | 0.57 | 0.43, 0.72 | < .001
BE-AR | loser-loser | 0.12 | 0.09, 0.14 | < .001
BE-BE | loser-winner | -0.03 | -0.04, -0.01 | < .001
BE-AE | loser-winner | -0.06 | -0.08, -0.05 | < .001
BE-BR | loser-winner | 0.69 | 0.60, 0.77 | < .001
BE-AR | loser-winner | -0.06 | -0.08, -0.05 | < .001
AE-BR | loser-loser | 0.57 | 0.42, 0.71 | < .001
AE-AR | loser-loser | 0.11 | 0.08, 0.14 | < .001
AE-BE | loser-winner | -0.03 | -0.06, -0.01 | 0.003
AE-AE | loser-winner | -0.07 | -0.10, -0.05 | < .001
AE-BR | loser-winner | 0.68 | 0.59, 0.76 | < .001
AE-AR | loser-winner | -0.07 | -0.09, -0.04 | < .001
BR-AR | loser-loser | -0.46 | -0.60, -0.31 | < .001
BR-BE | loser-winner | -0.60 | -0.74, -0.46 | < .001
BR-AE | loser-winner | -0.64 | -0.78, -0.49 | < .001
BR-BR | loser-winner | 0.11 | -0.06, 0.28 | 0.205
BR-AR | loser-winner | -0.64 | -0.78, -0.49 | < .001
AR-BE | loser-winner | -0.14 | -0.16, -0.12 | < .001
AR-AE | loser-winner | -0.18 | -0.20, -0.15 | < .001
AR-BR | loser-winner | 0.57 | 0.48, 0.66 | < .001
AR-AR | loser-winner | -0.18 | -0.20, -0.15 | < .001
BE-AE | winner-winner | -0.04 | -0.05, -0.02 | < .001
BE-BR | winner-winner | 0.71 | 0.63, 0.80 | < .001
BE-AR | winner-winner | -0.04 | -0.05, -0.02 | < .001
AE-BR | winner-winner | 0.75 | 0.66, 0.83 | < .001
AE-AR | winner-winner | 2.17e-03 | -0.01, 0.02 | 0.800
BR-AR | winner-winner | -0.75 | -0.83, -0.66 | < .001
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88fit04aPh: [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
=====================================================================
# Average predicted values of Agency
Outcome: loser
Phase: BE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.45 | 0.44, 0.46
-0.50 | 0.48 | 0.47, 0.49
0.00 | 0.51 | 0.50, 0.52
0.50 | 0.54 | 0.53, 0.55
1.00 | 0.57 | 0.56, 0.59
Outcome: loser
Phase: AE
Time | Predicted | 95% CI
--------------------------------
-1.00 | 0.90 | 0.79, 1.01
-0.50 | 0.69 | 0.62, 0.75
0.00 | 0.47 | 0.45, 0.49
0.50 | 0.25 | 0.22, 0.29
1.00 | 0.04 | -0.05, 0.13
Outcome: loser
Phase: BR
Time | Predicted | 95% CI
--------------------------------
-1.00 | -1.20 | -1.69, -0.70
-0.50 | -0.59 | -0.89, -0.29
0.00 | 0.02 | -0.10, 0.13
0.50 | 0.62 | 0.55, 0.69
1.00 | 1.23 | 0.97, 1.48
Outcome: loser
Phase: AR
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.39 | 0.34, 0.44
-0.50 | 0.39 | 0.36, 0.43
0.00 | 0.39 | 0.37, 0.41
0.50 | 0.39 | 0.38, 0.40
1.00 | 0.39 | 0.38, 0.41
Outcome: winner
Phase: BE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.50 | 0.49, 0.51
-0.50 | 0.52 | 0.51, 0.52
0.00 | 0.54 | 0.53, 0.54
0.50 | 0.56 | 0.55, 0.57
1.00 | 0.58 | 0.56, 0.59
Outcome: winner
Phase: AE
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.84 | 0.77, 0.90
-0.50 | 0.69 | 0.66, 0.73
0.00 | 0.55 | 0.54, 0.56
0.50 | 0.41 | 0.39, 0.43
1.00 | 0.27 | 0.22, 0.32
Outcome: winner
Phase: BR
Time | Predicted | 95% CI
--------------------------------
-1.00 | -1.87 | -2.16, -1.58
-0.50 | -0.96 | -1.14, -0.78
0.00 | -0.05 | -0.12, 0.02
0.50 | 0.85 | 0.81, 0.90
1.00 | 1.76 | 1.61, 1.91
Outcome: winner
Phase: AR
Time | Predicted | 95% CI
-------------------------------
-1.00 | 0.63 | 0.60, 0.65
-0.50 | 0.60 | 0.58, 0.62
0.00 | 0.56 | 0.55, 0.58
0.50 | 0.53 | 0.53, 0.54
1.00 | 0.50 | 0.49, 0.51
=====================================================================
# (Average) Linear trend for Time
Outcome | Phase | Slope | 95% CI | p
---------------------------------------------------
loser | BE | 0.06 | 0.05, 0.07 | < .001
loser | AE | -0.43 | -0.53, -0.33 | < .001
loser | BR | 1.21 | 0.84, 1.58 | < .001
loser | AR | -1.19e-03 | -0.03, 0.03 | 0.936
winner | BE | 0.04 | 0.03, 0.05 | < .001
winner | AE | -0.29 | -0.35, -0.23 | < .001
winner | BR | 1.81 | 1.59, 2.03 | < .001
winner | AR | -0.07 | -0.08, -0.05 | < .001
=====================================================================
# (Average) Linear trend for Time
Outcome | Phase | Contrast | 95% CI | p
--------------------------------------------------------
loser-loser | BE-AE | 0.49 | 0.39, 0.59 | < .001
loser-loser | BE-BR | -1.15 | -1.52, -0.78 | < .001
loser-loser | BE-AR | 0.06 | 0.03, 0.09 | < .001
loser-winner | BE-BE | 0.02 | 0.01, 0.03 | 0.005
loser-winner | BE-AE | 0.34 | 0.28, 0.40 | < .001
loser-winner | BE-BR | -1.76 | -1.98, -1.54 | < .001
loser-winner | BE-AR | 0.12 | 0.10, 0.14 | < .001
loser-loser | AE-BR | -1.64 | -2.03, -1.26 | < .001
loser-loser | AE-AR | -0.43 | -0.53, -0.33 | < .001
loser-winner | AE-BE | -0.47 | -0.57, -0.37 | < .001
loser-winner | AE-AE | -0.15 | -0.26, -0.03 | 0.013
loser-winner | AE-BR | -2.25 | -2.49, -2.01 | < .001
loser-winner | AE-AR | -0.37 | -0.47, -0.27 | < .001
loser-loser | BR-AR | 1.21 | 0.84, 1.58 | < .001
loser-winner | BR-BE | 1.17 | 0.80, 1.55 | < .001
loser-winner | BR-AE | 1.50 | 1.12, 1.87 | < .001
loser-winner | BR-BR | -0.61 | -1.04, -0.17 | 0.007
loser-winner | BR-AR | 1.28 | 0.90, 1.65 | < .001
loser-winner | AR-BE | -0.04 | -0.07, -0.01 | 0.015
loser-winner | AR-AE | 0.29 | 0.22, 0.35 | < .001
loser-winner | AR-BR | -1.82 | -2.04, -1.59 | < .001
loser-winner | AR-AR | 0.07 | 0.03, 0.10 | < .001
winner-winner | BE-AE | 0.32 | 0.26, 0.38 | < .001
winner-winner | BE-BR | -1.78 | -2.00, -1.56 | < .001
winner-winner | BE-AR | 0.10 | 0.09, 0.12 | < .001
winner-winner | AE-BR | -2.10 | -2.33, -1.87 | < .001
winner-winner | AE-AR | -0.22 | -0.28, -0.16 | < .001
winner-winner | BR-AR | 1.88 | 1.66, 2.10 | < .001
ggeff$test3 <- ggeffects::test_predictions(ggeff$pred0, test="pairwise", p_adjust="fdr", collapse_levels=TRUE)
## cat0(sep0)
## print(ggeff$test3, n = Inf)
ggeff$test4 <- ggeff$test3 %>% as_tibble() %>%
dplyr::mutate(
PhaseDashCount = str_count(Phase, fixed("-")),
OutcomeDashCount = str_count(Outcome, fixed("-")),
TotalDashCount = PhaseDashCount + OutcomeDashCount,
) %>%
dplyr::filter(TotalDashCount==1) %>%
dplyr::select(-TotalDashCount) %>%
dplyr::arrange(PhaseDashCount, Outcome, Phase) %>%
dplyr::select(-c(PhaseDashCount, OutcomeDashCount)) %>%
identity()
print(ggeff$test4)# A tibble: 16 × 7
Time Outcome Phase Contrast conf.low conf.high p.value
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 slope loser-winner AE -0.147 -0.262 -0.0321 1.27e- 2
2 slope loser-winner AR 0.0652 0.0321 0.0982 1.34e- 4
3 slope loser-winner BE 0.0204 0.00628 0.0345 5.18e- 3
4 slope loser-winner BR -0.605 -1.04 -0.172 6.62e- 3
5 slope loser AE-AR -0.432 -0.535 -0.330 2.45e-16
6 slope loser AE-BR -1.64 -2.03 -1.26 1.47e-16
7 slope loser BE-AE 0.491 0.392 0.590 7.06e-22
8 slope loser BE-AR 0.0582 0.0284 0.0881 1.53e- 4
9 slope loser BE-BR -1.15 -1.52 -0.779 1.82e- 9
10 slope loser BR-AR 1.21 0.836 1.58 3.23e-10
11 slope winner AE-AR -0.220 -0.281 -0.160 1.55e-12
12 slope winner AE-BR -2.10 -2.33 -1.87 5.37e-72
13 slope winner BE-AE 0.323 0.264 0.382 2.74e-26
14 slope winner BE-AR 0.103 0.0869 0.119 2.74e-35
15 slope winner BE-BR -1.78 -2.00 -1.56 9.36e-56
16 slope winner BR-AR 1.88 1.66 2.10 7.56e-62
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=24, height=72, limitsize = FALSE)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-Fig.svg"), plot = gg88 + cogsys::theme2, width=24, height=96, limitsize = FALSE)
gg88Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
fit04xPh: Time x Phase x Outcomecount8 = 15e3
count8 = 1e3
df8 <- df0 %>%
## dplyr::group_by(Phase) %>% ## CAUTION
## dplyr::slice_sample(n=count8) %>% ## CAUTION
bruceR::grand_mean_center(
vars=c("Agency", "Time"),
std=FALSE,
add.suffix="C") %>%
identity()
contrasts(df8$Phase) <- contr.sum(levels(df8$Phase))
contrasts(df8$Outcome) <- contr.sum(levels(df8$Outcome))
contrasts(df8$Phase) [,1] [,2] [,3]
BE 1 0 0
AE 0 1 0
BR 0 0 1
AR -1 -1 -1
[,1]
loser 1
winner -1
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
Data: df8
Control: control
REML criterion at convergence: 23562.2
Scaled residuals:
Min 1Q Median 3Q Max
-8.1902 -0.5632 -0.0074 0.5639 7.3864
Random effects:
Groups Name Variance Std.Dev. Corr
Name (Intercept) 0.004849 0.06963
TimeC 0.003347 0.05786 -0.12
Residual 0.065916 0.25674
Number of obs: 169997, groups: Name, 870
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -1.430e-01 1.111e-02 1.138e+05 -12.873 < 2e-16 ***
TimeC 2.911e-01 2.871e-02 1.687e+05 10.141 < 2e-16 ***
Phase1 1.673e-01 1.088e-02 1.689e+05 15.372 < 2e-16 ***
Phase2 1.815e-01 1.164e-02 1.688e+05 15.591 < 2e-16 ***
Phase3 -4.760e-01 3.189e-02 1.689e+05 -14.925 < 2e-16 ***
Outcome1 -2.021e-02 1.111e-02 1.138e+05 -1.819 0.06893 .
TimeC:Phase1 -2.443e-01 2.870e-02 1.689e+05 -8.510 < 2e-16 ***
TimeC:Phase2 -6.511e-01 3.535e-02 1.690e+05 -18.419 < 2e-16 ***
TimeC:Phase3 1.220e+00 8.317e-02 1.689e+05 14.671 < 2e-16 ***
TimeC:Outcome1 -8.335e-02 2.871e-02 1.687e+05 -2.904 0.00369 **
Phase1:Outcome1 7.600e-03 1.088e-02 1.689e+05 0.698 0.48496
Phase2:Outcome1 -1.568e-02 1.164e-02 1.688e+05 -1.347 0.17804
Phase3:Outcome1 7.591e-02 3.189e-02 1.689e+05 2.380 0.01730 *
TimeC:Phase1:Outcome1 9.355e-02 2.870e-02 1.689e+05 3.259 0.00112 **
TimeC:Phase2:Outcome1 9.791e-03 3.535e-02 1.690e+05 0.277 0.78180
TimeC:Phase3:Outcome1 -2.193e-01 8.317e-02 1.689e+05 -2.636 0.00838 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
# R2 for Mixed Models
Conditional R2: 0.102
Marginal R2: 0.021
---------------------------------------------------------------------
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
# Intraclass Correlation Coefficient
Adjusted ICC: 0.083
Unadjusted ICC: 0.081
---------------------------------------------------------------------
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
Model contains random slopes. Cannot compute accurate ICCs by group
factors.
# ICC by Group
Group | ICC
-------------
Name | 0.067
---------------------------------------------------------------------
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
do_checks <- TRUE
do_checks <- FALSE
if (do_checks) {
knitr::opts_chunk$set(fig.width=unit(8,"cm"), fig.height=unit(24,"cm"))
suppressWarnings(rm(list = ls(pattern = "^check8")))
check8 <- performance::check_model(get(model))
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- plot(check8)
## gg88
}fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
fit04xPh: [df8] AgencyC ~ (TimeC | Name) + TimeC * Phase * Outcome
Warning: Minimum value of original data is not included in the
replicated data.
Model may not capture the variation of the data.Warning: Maximum value of original data is not included in the
replicated data.
Model may not capture the variation of the data.
file = file.path(
ofd4, "summary-performance-score.png")
perf0 <- performance::compare_performance(
fit01aPh, # [df0] Agency ~ (1 | Name) + 1
fit02aPh, # [df0] Agency ~ (Time | Name) + Time
fit03aPh, # [df0] Agency ~ (Time | Name) + Time * Phase
fit04aPh, # [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
## CAUTION: COMMA
rank = TRUE, verbose = FALSE)
perf0 %>% performance::print_html()| Comparison of Model Performance Indices | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Name | Model | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma | AIC weights | AICc weights | BIC weights | Performance-Score |
| fit04aPh | lmerModLmerTest | 0.10 | 0.02 | 0.08 | 0.26 | 0.26 | 1.00 | 1.00 | 1.00 | 84.61% |
| fit03aPh | lmerModLmerTest | 0.11 | 6.03e-03 | 0.10 | 0.26 | 0.26 | 2.51e-118 | 2.51e-118 | 7.03e-101 | 52.18% |
| fit02aPh | lmerModLmerTest | 0.10 | 4.62e-04 | 0.10 | 0.26 | 0.26 | 9.57e-321 | 9.58e-321 | 3.26e-290 | 40.78% |
| fit01aPh | lmerModLmerTest | 0.08 | 0.00 | 0.08 | 0.26 | 0.26 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.99% |
| NA | ||||||||||
| Comparison of Model Performance Indices | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Name | Model | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma | AIC weights | AICc weights | BIC weights | Performance-Score |
| fit03aPh | lmerModLmerTest | 0.11 | 6.03e-03 | 0.10 | 0.26 | 0.26 | 2.51e-118 | 2.51e-118 | 7.03e-101 | 52.18% |
| fit02aPh | lmerModLmerTest | 0.10 | 4.62e-04 | 0.10 | 0.26 | 0.26 | 9.57e-321 | 9.58e-321 | 3.26e-290 | 40.78% |
| fit04aPh | lmerModLmerTest | 0.10 | 0.02 | 0.08 | 0.26 | 0.26 | 1.00 | 1.00 | 1.00 | 84.61% |
| fit01aPh | lmerModLmerTest | 0.08 | 0.00 | 0.08 | 0.26 | 0.26 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.99% |
| NA | ||||||||||
| Comparison of Model Performance Indices | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Name | Model | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma | AIC weights | AICc weights | BIC weights | Performance-Score |
| fit04aPh | lmerModLmerTest | 0.10 | 0.02 | 0.08 | 0.26 | 0.26 | 1.00 | 1.00 | 1.00 | 84.61% |
| fit03aPh | lmerModLmerTest | 0.11 | 6.03e-03 | 0.10 | 0.26 | 0.26 | 2.51e-118 | 2.51e-118 | 7.03e-101 | 52.18% |
| fit02aPh | lmerModLmerTest | 0.10 | 4.62e-04 | 0.10 | 0.26 | 0.26 | 9.57e-321 | 9.58e-321 | 3.26e-290 | 40.78% |
| fit01aPh | lmerModLmerTest | 0.08 | 0.00 | 0.08 | 0.26 | 0.26 | 0.00e+00 | 0.00e+00 | 0.00e+00 | 0.99% |
| NA | ||||||||||
model <- "fit01aPh" # [df0] Agency ~ (1 | Name) + 1
model <- "fit02aPh" # [df0] Agency ~ (Time | Name) + Time
model <- "fit03aPh" # [df0] Agency ~ (Time | Name) + Time * Phase
model <- "fit04aPh" # [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
cat0(effectsize::interpret_r2(performance::r2(get(model))$R2_conditional, rules="cohen1988"))weak
weak
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- sjPlot::plot_models(
## CAUTION the null model can not be used here
## Thus to keep the numbers consistent I have
## used model 02 as an input twice
## fit01aPh, # [df0] Agency ~ (Time | Name) + Time
fit02aPh, # [df0] Agency ~ (Time | Name) + Time
fit03aPh, # [df0] Agency ~ (Time | Name) + Time * Phase
fit04aPh, # [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
m.labels = c("Model 2", "Model 3", "Model 4"),
spacing=1,
dot.size=1
) + line0h
ggsave(
file = file.path(ofd4, "summary-plot-models-i0001-base.png"),
plot = gg88,
width=5,
height=4)
knitr::opts_chunk$set(fig.width=unit(12,"cm"), fig.height=unit(16,"cm"))
gg88## library(sjPlot)
## library(sjmisc)
## library(sjlabelled)
file <- file.path(ofd4, "summary-tab-model-i0001-base.html")
sjPlot::tab_model(
fit01aPh, # [df0] Agency ~ (1 | Name) + 1
fit02aPh, # [df0] Agency ~ (Time | Name) + Time
fit03aPh, # [df0] Agency ~ (Time | Name) + Time * Phase
fit04aPh, # [df0] Agency ~ (Time | Name) + Time * Phase * Outcome
show.reflvl = FALSE,
show.intercept = TRUE,
show.p = FALSE,
p.style = "numeric_stars",
dv.labels = c("Model 1", "Model 2", "Model 3", "Model 4"),
wrap.labels = 225,
file = file)| Model 1 | Model 2 | Model 3 | Model 4 | |||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | Estimates | CI | Estimates | CI | Estimates | CI |
| (Intercept) | 0.50 *** | 0.49 – 0.50 | 0.50 *** | 0.49 – 0.50 | 0.53 *** | 0.52 – 0.53 | 0.52 *** | 0.51 – 0.52 |
| Time | -0.01 *** | -0.02 – -0.01 | 0.04 *** | 0.04 – 0.05 | 0.06 *** | 0.05 – 0.07 | ||
| Phase [AE] | -0.00 | -0.01 – 0.00 | -0.04 *** | -0.06 – -0.03 | ||||
| Phase [BR] | -0.57 *** | -0.63 – -0.51 | -0.50 *** | -0.61 – -0.38 | ||||
| Phase [AR] | -0.01 | -0.02 – 0.00 | -0.12 *** | -0.14 – -0.10 | ||||
| Time × Phase [AE] | -0.36 *** | -0.41 – -0.31 | -0.49 *** | -0.59 – -0.39 | ||||
| Time × Phase [BR] | 1.61 *** | 1.42 – 1.80 | 1.15 *** | 0.78 – 1.52 | ||||
| Time × Phase [AR] | -0.10 *** | -0.11 – -0.09 | -0.06 *** | -0.09 – -0.03 | ||||
| Outcome [winner] | 0.02 *** | 0.01 – 0.04 | ||||||
| Time × Outcome [winner] | -0.02 ** | -0.03 – -0.01 | ||||||
| Phase [AE] × Outcome [winner] | 0.06 *** | 0.04 – 0.08 | ||||||
| Phase [BR] × Outcome [winner] | -0.09 | -0.23 – 0.04 | ||||||
| Phase [AR] × Outcome [winner] | 0.15 *** | 0.12 – 0.17 | ||||||
| (Time × Phase [AE]) × Outcome [winner] | 0.17 ** | 0.05 – 0.28 | ||||||
| (Time × Phase [BR]) × Outcome [winner] | 0.63 ** | 0.19 – 1.06 | ||||||
| (Time × Phase [AR]) × Outcome [winner] | -0.04 ** | -0.08 – -0.01 | ||||||
| Random Effects | ||||||||
| σ2 | 0.07 | 0.07 | 0.07 | 0.07 | ||||
| τ00 | 0.01 Name | 0.01 Name | 0.01 Name | 0.00 Name | ||||
| τ11 | 0.00 Name.Time | 0.00 Name.Time | 0.00 Name.Time | |||||
| ρ01 | 0.18 Name | 0.18 Name | -0.07 Name | |||||
| ICC | 0.08 | 0.10 | 0.10 | 0.08 | ||||
| N | 870 Name | 870 Name | 870 Name | 870 Name | ||||
| Observations | 169997 | 169997 | 169997 | 169997 | ||||
| Marginal R2 / Conditional R2 | 0.000 / 0.084 | 0.000 / 0.102 | 0.006 / 0.107 | 0.021 / 0.102 | ||||
| * p<0.05 ** p<0.01 *** p<0.001 | ||||||||
./data/20240428T200156-politicians-aux-analysis/n0001-init//n0001-models-phase-i0021-all/summary-tab-model-i0001-base.html
[Click to reveal](./data/20240428T200156-politicians-aux-analysis/n0001-init//n0001-models-phase-i0021-all/summary-tab-model-i0001-base.html)We fitted a linear mixed model (estimated using REML and Nelder-Mead optimizer)
to predict Agency with Time, Phase and Outcome (formula: Agency ~ Time * Phase
* Outcome). The model included Time as random effects (formula: ~Time | Name).
The model's total explanatory power is weak (conditional R2 = 0.10) and the
part related to the fixed effects alone (marginal R2) is of 0.02. The model's
intercept, corresponding to Time = 0, Phase = BE and Outcome = loser, is at
0.52 (95% CI [0.51, 0.52], t(169977) = 115.77, p < .001). Within this model:
- The effect of Time is statistically significant and positive (beta = 0.06,
95% CI [0.05, 0.07], t(169977) = 10.19, p < .001; Std. beta = 0.12, 95% CI
[0.10, 0.14])
- The effect of Phase [AE] is statistically significant and negative (beta =
-0.04, 95% CI [-0.06, -0.03], t(169977) = -5.52, p < .001; Std. beta = -0.03,
95% CI [-0.11, 0.04])
- The effect of Phase [BR] is statistically significant and negative (beta =
-0.50, 95% CI [-0.61, -0.38], t(169977) = -8.24, p < .001; Std. beta = -2.12,
95% CI [-2.65, -1.60])
- The effect of Phase [AR] is statistically significant and negative (beta =
-0.12, 95% CI [-0.14, -0.10], t(169977) = -11.56, p < .001; Std. beta = -0.43,
95% CI [-0.51, -0.35])
- The effect of Outcome [winner] is statistically significant and positive
(beta = 0.02, 95% CI [0.01, 0.04], t(169977) = 4.07, p < .001; Std. beta =
0.09, 95% CI [0.05, 0.13])
- The effect of Time × Phase [AE] is statistically significant and negative
(beta = -0.49, 95% CI [-0.59, -0.39], t(169977) = -9.71, p < .001; Std. beta =
-1.04, 95% CI [-1.25, -0.83])
- The effect of Time × Phase [BR] is statistically significant and positive
(beta = 1.15, 95% CI [0.78, 1.52], t(169977) = 6.05, p < .001; Std. beta =
2.43, 95% CI [1.64, 3.22])
- The effect of Time × Phase [AR] is statistically significant and negative
(beta = -0.06, 95% CI [-0.09, -0.03], t(169977) = -3.82, p < .001; Std. beta =
-0.12, 95% CI [-0.19, -0.06])
- The effect of Time × Outcome [winner] is statistically significant and
negative (beta = -0.02, 95% CI [-0.03, -6.28e-03], t(169977) = -2.83, p =
0.005; Std. beta = -0.04, 95% CI [-0.07, -0.01])
- The effect of Phase [AE] × Outcome [winner] is statistically significant and
positive (beta = 0.06, 95% CI [0.04, 0.08], t(169977) = 6.32, p < .001; Std.
beta = 0.17, 95% CI [0.08, 0.26])
- The effect of Phase [BR] × Outcome [winner] is statistically non-significant
and negative (beta = -0.09, 95% CI [-0.23, 0.04], t(169977) = -1.34, p = 0.179;
Std. beta = -0.50, 95% CI [-1.12, 0.11])
- The effect of Phase [AR] × Outcome [winner] is statistically significant and
positive (beta = 0.15, 95% CI [0.12, 0.17], t(169977) = 12.55, p < .001; Std.
beta = 0.56, 95% CI [0.47, 0.65])
- The effect of (Time × Phase [AE]) × Outcome [winner] is statistically
significant and positive (beta = 0.17, 95% CI [0.05, 0.28], t(169977) = 2.85, p
= 0.004; Std. beta = 0.35, 95% CI [0.11, 0.60])
- The effect of (Time × Phase [BR]) × Outcome [winner] is statistically
significant and positive (beta = 0.63, 95% CI [0.19, 1.06], t(169977) = 2.83, p
= 0.005; Std. beta = 1.32, 95% CI [0.41, 2.24])
- The effect of (Time × Phase [AR]) × Outcome [winner] is statistically
significant and negative (beta = -0.04, 95% CI [-0.08, -0.01], t(169977) =
-2.59, p = 0.010; Std. beta = -0.09, 95% CI [-0.17, -0.02])
Standardized parameters were obtained by fitting the model on a standardized
version of the dataset. 95% Confidence Intervals (CIs) and p-values were
computed using a Wald t-distribution approximation.
[1] 60000 22
# A tibble: 4 × 2
Phase Count
<fct> <int>
1 BE 15000
2 AE 15000
3 BR 15000
4 AR 15000
xFit05aPhLikes
xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
Family: truncated_poisson ( log )
Formula: LikeCount ~ Agency * Phase + (1 | Name)
Zero inflation: ~Agency * Phase
Data: df5
AIC BIC logLik deviance df.resid
211146817 211146970 -105573392 211146783 59983
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Name (Intercept) 3.97 1.993
Number of obs: 60000, groups: Name, 845
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.8581581 0.0690181 55.9 <2e-16 ***
Agency 0.0444432 0.0009483 46.9 <2e-16 ***
PhaseAE 0.7681026 0.0006708 1145.1 <2e-16 ***
PhaseBR 1.0178189 0.0006548 1554.3 <2e-16 ***
PhaseAR 0.6531470 0.0007137 915.2 <2e-16 ***
Agency:PhaseAE 0.0891286 0.0010687 83.4 <2e-16 ***
Agency:PhaseBR 0.3487010 0.0010217 341.3 <2e-16 ***
Agency:PhaseAR -0.3890907 0.0011529 -337.5 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.01127 0.06779 -44.42 < 2e-16 ***
Agency -0.69005 0.12165 -5.67 1.41e-08 ***
PhaseAE 0.02190 0.09269 0.24 0.813210
PhaseBR -0.26488 0.09474 -2.80 0.005177 **
PhaseAR -0.36530 0.10327 -3.54 0.000404 ***
Agency:PhaseAE -0.41565 0.17241 -2.41 0.015915 *
Agency:PhaseBR -0.24252 0.17176 -1.41 0.157968
Agency:PhaseAR -0.42872 0.19291 -2.22 0.026259 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
# R2 for Mixed Models
Conditional R2: 1.000
Marginal R2: 0.048
---------------------------------------------------------------------
xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
# Intraclass Correlation Coefficient
Adjusted ICC: 1.000
Unadjusted ICC: 0.952
---------------------------------------------------------------------
xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
# ICC by Group
Group | ICC
-------------
Name | 1.000
---------------------------------------------------------------------
xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Phase: BE
Agency | Predicted | 95% CI
------------------------------------
-3 | 439.89 | 338.63, 541.15
-2 | 534.69 | 483.58, 585.79
-1 | 608.64 | 589.55, 627.73
0 | 665.97 | 661.75, 670.19
1 | 712.90 | 709.97, 715.84
2 | 754.36 | 750.18, 758.54
3 | 793.48 | 788.67, 798.29
Phase: AE
Agency | Predicted | 95% CI
-------------------------------------
-3 | 422.54 | 224.64, 620.44
-2 | 790.18 | 648.74, 931.61
-1 | 1144.00 | 1093.44, 1194.55
0 | 1434.09 | 1425.54, 1442.64
1 | 1693.30 | 1688.14, 1698.46
2 | 1956.72 | 1951.46, 1961.98
3 | 2244.57 | 2238.48, 2250.66
Phase: BR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 367.02 | 254.79, 479.25
-2 | 708.09 | 629.73, 786.44
-1 | 1190.71 | 1155.91, 1225.50
0 | 1863.14 | 1854.29, 1871.98
1 | 2822.79 | 2814.92, 2830.66
2 | 4219.82 | 4208.77, 4230.87
3 | 6274.35 | 6259.58, 6289.12
Phase: AR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 1907.02 | 972.47, 2841.56
-2 | 2026.34 | 1682.63, 2370.05
-1 | 1715.75 | 1648.67, 1782.83
0 | 1298.33 | 1291.71, 1304.95
1 | 940.76 | 938.34, 943.18
2 | 671.50 | 669.68, 673.32
3 | 476.91 | 475.28, 478.55
=====================================================================
# (Average) Linear trend for Agency
Phase | Slope | 95% CI | p
------------------------------------------
BE | 37.85 | 26.15, 49.56 | < .001
AE | 188.95 | 172.41, 205.49 | < .001
BR | 652.36 | 641.39, 663.33 | < .001
AR | -112.11 | -219.96, -4.27 | 0.042
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Phase | Contrast | 95% CI | p
--------------------------------------------
BE-AE | -151.10 | -171.36, -130.84 | < .001
BE-BR | -614.51 | -630.55, -598.47 | < .001
BE-AR | 149.96 | 41.48, 258.44 | 0.007
AE-BR | -463.41 | -483.25, -443.56 | < .001
AE-AR | 301.06 | 191.96, 410.17 | < .001
BR-AR | 764.47 | 656.07, 872.87 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Phase | Predicted | 95% CI
------------------------------------
BE | 689.64 | 687.50, 691.79
AE | 1563.30 | 1558.93, 1567.66
BR | 2322.87 | 2317.19, 2328.56
AR | 1116.05 | 1113.11, 1118.98
=====================================================================
Phase | Predicted | 95% CI | p
---------------------------------------------
BE | 689.64 | 687.50, 691.79 | < .001
AE | 1563.30 | 1558.93, 1567.66 | < .001
BR | 2322.87 | 2317.19, 2328.56 | < .001
AR | 1116.05 | 1113.11, 1118.98 | < .001
Predictions are presented as counts.
=====================================================================
# Pairwise comparisons
Phase | Contrast | 95% CI | p
----------------------------------------------
BE-AE | -873.65 | -878.53, -868.78 | < .001
BE-BR | -1633.23 | -1639.32, -1627.14 | < .001
BE-AR | -426.40 | -430.05, -422.75 | < .001
AE-BR | -759.58 | -766.77, -752.39 | < .001
AE-AR | 447.25 | 441.97, 452.52 | < .001
BR-AR | 1206.83 | 1200.41, 1213.24 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05aPhLikes: [df5] LikeCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Agency | Predicted | 95% CI
-------------------------------------
-3 | 767.94 | 532.79, 1003.10
-2 | 1016.19 | 920.78, 1111.61
-1 | 1192.23 | 1168.10, 1216.37
0 | 1373.61 | 1369.70, 1377.53
1 | 1638.24 | 1635.49, 1641.00
2 | 2045.81 | 2042.28, 2049.35
3 | 2660.66 | 2656.07, 2665.24
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
--------------------------------
268.82 | 263.37, 274.28 | < .001
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
--------------------------------
268.82 | 263.37, 274.28 | < .001
Slopes are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit06aPhRetweets
xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
Family: truncated_poisson ( log )
Formula: RetweetCount ~ Agency * Phase + (1 | Name)
Zero inflation: ~Agency * Phase
Data: df5
AIC BIC logLik deviance df.resid
36974200 36974353 -18487083 36974166 59983
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Name (Intercept) 3.23 1.797
Number of obs: 60000, groups: Name, 845
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.849823 0.062872 45.3 < 2e-16 ***
Agency 0.011655 0.001890 6.2 7.04e-10 ***
PhaseAE 0.238168 0.001431 166.5 < 2e-16 ***
PhaseBR 0.615614 0.001350 455.9 < 2e-16 ***
PhaseAR 0.335873 0.001485 226.2 < 2e-16 ***
Agency:PhaseAE 0.166094 0.002284 72.7 < 2e-16 ***
Agency:PhaseBR 0.259858 0.002118 122.7 < 2e-16 ***
Agency:PhaseAR -0.371597 0.002428 -153.1 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.56827 0.05591 -45.94 < 2e-16 ***
Agency -0.50021 0.09756 -5.13 2.94e-07 ***
PhaseAE 0.28850 0.07332 3.93 8.32e-05 ***
PhaseBR 0.13146 0.07268 1.81 0.070479 .
PhaseAR -0.01566 0.07860 -0.20 0.842041
Agency:PhaseAE -0.71883 0.13485 -5.33 9.79e-08 ***
Agency:PhaseBR -0.39487 0.12916 -3.06 0.002234 **
Agency:PhaseAR -0.54609 0.14311 -3.82 0.000136 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
# R2 for Mixed Models
Conditional R2: 1.000
Marginal R2: 0.026
---------------------------------------------------------------------
xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
# Intraclass Correlation Coefficient
Adjusted ICC: 1.000
Unadjusted ICC: 0.974
---------------------------------------------------------------------
xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
# ICC by Group
Group | ICC
-------------
Name | 1.000
---------------------------------------------------------------------
xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of RetweetCount
Phase: BE
Agency | Predicted | 95% CI
-----------------------------------
-3 | 127.72 | 106.01, 149.43
-2 | 143.69 | 131.89, 155.48
-1 | 155.96 | 150.90, 161.02
0 | 165.08 | 163.74, 166.42
1 | 171.83 | 170.79, 172.88
2 | 176.94 | 175.14, 178.73
3 | 180.96 | 178.70, 183.23
Phase: AE
Agency | Predicted | 95% CI
-----------------------------------
-3 | 26.66 | 13.59, 39.73
-2 | 72.79 | 55.78, 89.80
-1 | 140.25 | 131.05, 149.44
0 | 204.58 | 202.79, 206.38
1 | 261.47 | 260.34, 262.60
2 | 318.92 | 317.41, 320.42
3 | 383.34 | 380.86, 385.82
Phase: BR
Agency | Predicted | 95% CI
-----------------------------------
-3 | 63.84 | 43.73, 83.96
-2 | 125.42 | 108.31, 142.53
-1 | 206.51 | 197.93, 215.10
0 | 302.46 | 300.21, 304.70
1 | 416.61 | 414.73, 418.50
2 | 557.95 | 555.21, 560.70
3 | 738.28 | 734.33, 742.23
Phase: AR
Agency | Predicted | 95% CI
-----------------------------------
-3 | 266.98 | 148.88, 385.08
-2 | 316.88 | 257.57, 376.19
-1 | 293.33 | 278.14, 308.53
0 | 231.21 | 229.40, 233.01
1 | 169.03 | 168.30, 169.76
2 | 119.98 | 119.30, 120.66
3 | 84.26 | 83.60, 84.93
=====================================================================
# (Average) Linear trend for Agency
Phase | Slope | 95% CI | p
---------------------------------------
BE | 5.82 | 3.18, 8.45 | < .001
AE | 36.70 | 35.81, 37.59 | < .001
BR | 72.78 | 70.91, 74.65 | < .001
AR | -13.80 | -26.06, -1.54 | 0.027
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Phase | Contrast | 95% CI | p
------------------------------------------
BE-AE | -30.88 | -33.66, -28.10 | < .001
BE-BR | -66.96 | -70.20, -63.73 | < .001
BE-AR | 19.62 | 7.08, 32.16 | 0.002
AE-BR | -36.08 | -38.15, -34.01 | < .001
AE-AR | 50.50 | 38.21, 62.79 | < .001
BR-AR | 86.58 | 74.18, 98.99 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of RetweetCount
Phase | Predicted | 95% CI
----------------------------------
BE | 168.53 | 167.83, 169.23
AE | 233.02 | 232.11, 233.93
BR | 358.09 | 356.72, 359.45
AR | 199.73 | 198.94, 200.52
=====================================================================
Phase | Predicted | 95% CI | p
-------------------------------------------
BE | 168.53 | 167.83, 169.23 | < .001
AE | 233.02 | 232.11, 233.93 | < .001
BR | 358.09 | 356.72, 359.45 | < .001
AR | 199.73 | 198.94, 200.52 | < .001
Predictions are presented as counts.
=====================================================================
# Pairwise comparisons
Phase | Contrast | 95% CI | p
--------------------------------------------
BE-AE | -64.49 | -65.65, -63.33 | < .001
BE-BR | -189.56 | -191.11, -188.01 | < .001
BE-AR | -31.20 | -32.27, -30.14 | < .001
AE-BR | -125.07 | -126.72, -123.41 | < .001
AE-AR | 33.29 | 32.07, 34.50 | < .001
BR-AR | 158.36 | 156.77, 159.94 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit06aPhRetweets: [df5] RetweetCount ~ Agency * Phase + (1 | Name)
=====================================================================
# Average predicted counts of RetweetCount
Agency | Predicted | 95% CI
-----------------------------------
-3 | 114.59 | 85.01, 144.16
-2 | 160.17 | 144.15, 176.18
-1 | 198.88 | 193.68, 204.08
0 | 230.98 | 230.03, 231.93
1 | 265.63 | 264.96, 266.30
2 | 310.95 | 309.99, 311.91
3 | 372.26 | 370.86, 373.65
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
-----------------------------
35.04 | 33.72, 36.36 | < .001
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
-----------------------------
35.04 | 33.72, 36.36 | < .001
Slopes are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05oPhLikes (+Outcome)Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
problem; function evaluation limit reached without convergence (9). See
vignette('troubleshooting'), help('diagnose')
xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
Family: truncated_poisson ( log )
Formula: LikeCount ~ Agency * Phase * Outcome + (1 | Name)
Zero inflation: ~Agency * Phase * Outcome
Data: df5
AIC BIC logLik deviance df.resid
210486627 210486924 -105243280 210486561 59967
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Name (Intercept) 3.615 1.901
Number of obs: 60000, groups: Name, 845
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.369100 0.101596 33.2 < 2e-16 ***
Agency -0.053785 0.002022 -26.6 < 2e-16 ***
PhaseAE 0.460565 0.001648 279.4 < 2e-16 ***
PhaseBR 0.453566 0.001482 306.0 < 2e-16 ***
PhaseAR 0.641751 0.001520 422.2 < 2e-16 ***
Outcomewinner 0.939564 0.133452 7.0 1.92e-12 ***
Agency:PhaseAE 0.319924 0.002754 116.1 < 2e-16 ***
Agency:PhaseBR 0.278548 0.002488 111.9 < 2e-16 ***
Agency:PhaseAR -0.550491 0.002756 -199.7 < 2e-16 ***
Agency:Outcomewinner 0.153101 0.002291 66.8 < 2e-16 ***
PhaseAE:Outcomewinner 0.395588 0.001814 218.1 < 2e-16 ***
PhaseBR:Outcomewinner 0.690471 0.001658 416.4 < 2e-16 ***
PhaseAR:Outcomewinner 0.040590 0.001723 23.6 < 2e-16 ***
Agency:PhaseAE:Outcomewinner -0.291761 0.003002 -97.2 < 2e-16 ***
Agency:PhaseBR:Outcomewinner 0.007423 0.002740 2.7 0.00676 **
Agency:PhaseAR:Outcomewinner 0.176475 0.003042 58.0 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Zero-inflation model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.52762 0.08242 -30.669 < 2e-16 ***
Agency -0.62818 0.15006 -4.186 2.84e-05 ***
PhaseAE 0.44327 0.11008 4.027 5.65e-05 ***
PhaseBR 0.11842 0.11018 1.075 0.282
PhaseAR 0.19703 0.12039 1.637 0.102
Outcomewinner -1.10911 0.14707 -7.542 4.65e-14 ***
Agency:PhaseAE -0.02376 0.20907 -0.114 0.910
Agency:PhaseBR 0.23911 0.20535 1.164 0.244
Agency:PhaseAR 0.09670 0.23315 0.415 0.678
Agency:Outcomewinner 0.06739 0.25961 0.260 0.795
PhaseAE:Outcomewinner -0.91942 0.21342 -4.308 1.65e-05 ***
PhaseBR:Outcomewinner -1.40170 0.25511 -5.495 3.92e-08 ***
PhaseAR:Outcomewinner -1.29074 0.25067 -5.149 2.62e-07 ***
Agency:PhaseAE:Outcomewinner -0.17534 0.38579 -0.454 0.649
Agency:PhaseBR:Outcomewinner -0.37387 0.44640 -0.838 0.402
Agency:PhaseAR:Outcomewinner 0.15817 0.43757 0.361 0.718
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
# R2 for Mixed Models
Conditional R2: 1.000
Marginal R2: 0.132
---------------------------------------------------------------------
xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
# Intraclass Correlation Coefficient
Adjusted ICC: 1.000
Unadjusted ICC: 0.868
---------------------------------------------------------------------
xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
# ICC by Group
Group | ICC
-------------
Name | 1.000
---------------------------------------------------------------------
xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Outcome: loser
Phase: BE
Agency | Predicted | 95% CI
------------------------------------
-3 | 249.30 | 151.75, 346.85
-2 | 281.48 | 214.16, 348.80
-1 | 297.10 | 240.99, 353.22
0 | 299.75 | 245.55, 353.94
1 | 294.20 | 241.06, 347.34
2 | 284.21 | 232.82, 335.61
3 | 272.16 | 222.92, 321.39
Outcome: loser
Phase: AE
Agency | Predicted | 95% CI
------------------------------------
-3 | 122.86 | 63.85, 181.87
-2 | 206.62 | 149.37, 263.86
-1 | 317.37 | 255.31, 379.43
0 | 456.24 | 373.60, 538.89
1 | 628.66 | 514.89, 742.44
2 | 844.98 | 691.82, 998.15
3 | 1120.16 | 917.19, 1323.12
Outcome: loser
Phase: BR
Agency | Predicted | 95% CI
------------------------------------
-3 | 201.42 | 146.25, 256.58
-2 | 271.79 | 214.90, 328.69
-1 | 359.23 | 292.46, 426.01
0 | 467.40 | 382.88, 551.93
1 | 601.18 | 492.43, 709.92
2 | 766.88 | 627.70, 906.05
3 | 972.57 | 795.82, 1149.32
Outcome: loser
Phase: AR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 2547.63 | 1484.65, 3610.62
-2 | 1606.72 | 1198.13, 2015.32
-1 | 965.46 | 779.48, 1151.44
0 | 560.40 | 458.92, 661.88
1 | 317.87 | 260.31, 375.43
2 | 177.71 | 145.45, 209.96
3 | 98.51 | 80.66, 116.36
Outcome: winner
Phase: BE
Agency | Predicted | 95% CI
-------------------------------------
-3 | 538.52 | 432.76, 644.29
-2 | 628.21 | 557.91, 698.51
-1 | 716.82 | 655.42, 778.22
0 | 806.94 | 741.30, 872.58
1 | 901.13 | 827.94, 974.31
2 | 1001.58 | 920.09, 1083.07
3 | 1110.22 | 1019.80, 1200.63
Outcome: winner
Phase: AE
Agency | Predicted | 95% CI
-------------------------------------
-3 | 1146.68 | 869.00, 1424.36
-2 | 1405.74 | 1243.63, 1567.85
-1 | 1658.28 | 1518.08, 1798.49
0 | 1918.25 | 1762.45, 2074.05
1 | 2197.89 | 2019.54, 2376.24
2 | 2506.85 | 2303.38, 2710.32
3 | 2853.10 | 2621.51, 3084.68
Outcome: winner
Phase: BR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 773.00 | 657.39, 888.62
-2 | 1168.88 | 1059.91, 1277.85
-1 | 1743.19 | 1599.71, 1886.67
0 | 2581.20 | 2371.73, 2790.67
1 | 3808.28 | 3499.35, 4117.22
2 | 5608.50 | 5153.46, 6063.54
3 | 8252.18 | 7582.60, 8921.77
Outcome: winner
Phase: AR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 3654.95 | 3315.23, 3994.67
-2 | 2792.94 | 2556.53, 3029.35
-1 | 2131.07 | 1956.65, 2305.48
0 | 1624.25 | 1492.42, 1756.09
1 | 1236.96 | 1136.59, 1337.33
2 | 941.44 | 864.95, 1017.94
3 | 716.21 | 657.95, 774.47
=====================================================================
# (Average) Linear trend for Agency
Outcome | Phase | Slope | 95% CI | p
-----------------------------------------------------
loser | BE | 3.62 | -7.06, 14.31 | 0.507
loser | AE | 113.19 | 93.75, 132.63 | < .001
loser | BR | 87.36 | 71.81, 102.90 | < .001
loser | AR | -289.86 | -445.20, -134.52 | < .001
winner | BE | 64.09 | 49.90, 78.27 | < .001
winner | AE | 192.61 | 152.80, 232.43 | < .001
winner | BR | 885.71 | 795.66, 975.75 | < .001
winner | AR | -336.48 | -377.68, -295.28 | < .001
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Outcome | Phase | Contrast | 95% CI | p
--------------------------------------------------------------
loser-loser | BE-AE | -109.57 | -131.24, -87.89 | < .001
loser-loser | BE-BR | -83.74 | -102.16, -65.31 | < .001
loser-loser | BE-AR | 293.48 | 137.60, 449.36 | < .001
loser-winner | BE-BE | -60.47 | -78.43, -42.50 | < .001
loser-winner | BE-AE | -188.99 | -230.48, -147.50 | < .001
loser-winner | BE-BR | -882.09 | -973.33, -790.84 | < .001
loser-winner | BE-AR | 340.10 | 298.01, 382.20 | < .001
loser-loser | AE-BR | 25.83 | 15.51, 36.15 | < .001
loser-loser | AE-AR | 403.04 | 241.15, 564.94 | < .001
loser-winner | AE-BE | 49.10 | 20.57, 77.63 | < .001
loser-winner | AE-AE | -79.42 | -131.09, -27.76 | 0.003
loser-winner | AE-BR | -772.52 | -880.84, -664.20 | < .001
loser-winner | AE-AR | 449.67 | 420.63, 478.71 | < .001
loser-loser | BR-AR | 377.21 | 216.95, 537.48 | < .001
loser-winner | BR-BE | 23.27 | -1.71, 48.25 | 0.070
loser-winner | BR-AE | -105.25 | -153.96, -56.55 | < .001
loser-winner | BR-BR | -798.35 | -902.54, -694.15 | < .001
loser-winner | BR-AR | 423.84 | 392.41, 455.27 | < .001
loser-winner | AR-BE | -353.94 | -507.99, -199.90 | < .001
loser-winner | AR-AE | -482.47 | -637.09, -327.85 | < .001
loser-winner | AR-BR | -1175.56 | -1330.23, -1020.90 | < .001
loser-winner | AR-AR | 46.63 | -123.62, 216.87 | 0.591
winner-winner | BE-AE | -128.53 | -167.73, -89.32 | < .001
winner-winner | BE-BR | -821.62 | -906.24, -737.00 | < .001
winner-winner | BE-AR | 400.57 | 352.25, 448.88 | < .001
winner-winner | AE-BR | -693.09 | -772.10, -614.09 | < .001
winner-winner | AE-AR | 529.09 | 461.33, 596.86 | < .001
winner-winner | BR-AR | 1222.19 | 1096.38, 1347.99 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Outcome: loser
Phase | Predicted | 95% CI
------------------------------------
BE | 297.37 | 243.72, 351.03
AE | 536.90 | 439.04, 634.76
BR | 530.02 | 433.63, 626.41
AR | 438.30 | 360.29, 516.32
Outcome: winner
Phase | Predicted | 95% CI
------------------------------------
BE | 851.75 | 783.08, 920.42
AE | 2050.81 | 1885.79, 2215.83
BR | 3145.50 | 2896.33, 3394.67
AR | 1433.99 | 1315.77, 1552.21
=====================================================================
Phase | Outcome | Predicted | 95% CI | p
-------------------------------------------------------
BE | loser | 297.37 | 243.72, 351.03 | < .001
AE | loser | 536.90 | 439.04, 634.76 | < .001
BR | loser | 530.02 | 433.63, 626.41 | < .001
AR | loser | 438.30 | 360.29, 516.32 | < .001
BE | winner | 851.75 | 783.08, 920.42 | < .001
AE | winner | 2050.81 | 1885.79, 2215.83 | < .001
BR | winner | 3145.50 | 2896.33, 3394.67 | < .001
AR | winner | 1433.99 | 1315.77, 1552.21 | < .001
Predictions are presented as counts.
=====================================================================
# Pairwise comparisons
Phase | Outcome | Contrast | 95% CI | p
--------------------------------------------------------------
BE-AE | loser-loser | -239.53 | -283.99, -195.07 | < .001
BE-BR | loser-loser | -232.65 | -275.59, -189.71 | < .001
BE-AR | loser-loser | -140.93 | -165.69, -116.17 | < .001
BE-BE | loser-winner | -554.37 | -676.66, -432.09 | < .001
BE-AE | loser-winner | -1753.44 | -1972.08, -1534.80 | < .001
BE-BR | loser-winner | -2848.13 | -3150.92, -2545.34 | < .001
BE-AR | loser-winner | -1136.61 | -1308.46, -964.77 | < .001
AE-BR | loser-loser | 6.88 | -0.21, 13.97 | 0.057
AE-AR | loser-loser | 98.60 | 77.57, 119.63 | < .001
AE-BE | loser-winner | -314.84 | -481.29, -148.40 | < .001
AE-AE | loser-winner | -1513.91 | -1776.68, -1251.14 | < .001
AE-BR | loser-winner | -2608.60 | -2955.51, -2261.68 | < .001
AE-AR | loser-winner | -897.08 | -1113.07, -681.09 | < .001
BR-AR | loser-loser | 91.72 | 72.25, 111.18 | < .001
BR-BE | loser-winner | -321.72 | -486.71, -156.74 | < .001
BR-AE | loser-winner | -1520.79 | -1782.11, -1259.46 | < .001
BR-BR | loser-winner | -2615.48 | -2960.95, -2270.01 | < .001
BR-AR | loser-winner | -903.96 | -1118.50, -689.42 | < .001
AR-BE | loser-winner | -413.44 | -560.04, -266.84 | < .001
AR-AE | loser-winner | -1612.51 | -1855.44, -1369.57 | < .001
AR-BR | loser-winner | -2707.20 | -3034.27, -2380.12 | < .001
AR-AR | loser-winner | -995.68 | -1191.83, -799.53 | < .001
BE-AE | winner-winner | -1199.06 | -1295.54, -1102.59 | < .001
BE-BR | winner-winner | -2293.76 | -2474.34, -2113.17 | < .001
BE-AR | winner-winner | -582.24 | -631.95, -532.53 | < .001
AE-BR | winner-winner | -1094.69 | -1179.08, -1010.31 | < .001
AE-AR | winner-winner | 616.82 | 569.79, 663.86 | < .001
BR-AR | winner-winner | 1711.52 | 1580.48, 1842.55 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Phase: BE
Agency | Predicted | 95% CI
------------------------------------
-3 | 448.83 | 377.16, 520.50
-2 | 520.69 | 484.58, 556.80
-1 | 586.66 | 572.10, 601.23
0 | 649.66 | 645.99, 653.33
1 | 712.92 | 710.17, 715.67
2 | 779.13 | 774.52, 783.74
3 | 850.33 | 844.52, 856.15
Phase: AE
Agency | Predicted | 95% CI
-------------------------------------
-3 | 829.19 | 647.89, 1010.50
-2 | 1033.89 | 953.25, 1114.53
-1 | 1242.46 | 1214.14, 1270.79
0 | 1464.88 | 1458.71, 1471.06
1 | 1711.27 | 1706.52, 1716.03
2 | 1991.50 | 1984.39, 1998.61
3 | 2315.71 | 2307.08, 2324.34
Phase: BR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 595.75 | 527.51, 663.99
-2 | 890.69 | 852.55, 928.83
-1 | 1314.02 | 1296.42, 1331.63
0 | 1925.71 | 1920.40, 1931.02
1 | 2813.76 | 2808.63, 2818.88
2 | 4107.11 | 4097.28, 4116.94
3 | 5994.76 | 5979.38, 6010.15
Phase: AR
Agency | Predicted | 95% CI
-------------------------------------
-3 | 3311.57 | 2993.07, 3630.07
-2 | 2425.09 | 2324.34, 2525.84
-1 | 1769.61 | 1743.74, 1795.48
0 | 1294.35 | 1289.84, 1298.86
1 | 951.95 | 949.32, 954.58
2 | 704.61 | 701.23, 707.98
3 | 524.65 | 521.45, 527.85
=====================================================================
# (Average) Linear trend for Agency
Phase | Slope | 95% CI | p
-------------------------------------------
BE | 3.62 | -7.06, 14.31 | 0.507
AE | 113.19 | 93.75, 132.63 | < .001
BR | 87.36 | 71.81, 102.90 | < .001
AR | -289.86 | -445.20, -134.52 | < .001
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Phase | Contrast | 95% CI | p
-------------------------------------------
BE-AE | -109.57 | -131.24, -87.89 | < .001
BE-BR | -83.74 | -102.16, -65.31 | < .001
BE-AR | 293.48 | 137.60, 449.36 | < .001
AE-BR | 25.83 | 15.51, 36.15 | < .001
AE-AR | 403.04 | 241.15, 564.94 | < .001
BR-AR | 377.21 | 216.95, 537.48 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Phase | Predicted | 95% CI
------------------------------------
BE | 681.68 | 679.79, 683.57
AE | 1583.34 | 1580.07, 1586.61
BR | 2354.82 | 2351.53, 2358.12
AR | 1122.57 | 1120.33, 1124.81
=====================================================================
Phase | Predicted | 95% CI | p
---------------------------------------------
BE | 681.68 | 679.79, 683.57 | < .001
AE | 1583.34 | 1580.07, 1586.61 | < .001
BR | 2354.82 | 2351.53, 2358.12 | < .001
AR | 1122.57 | 1120.33, 1124.81 | < .001
Predictions are presented as counts.
=====================================================================
# Pairwise comparisons
Phase | Contrast | 95% CI | p
----------------------------------------------
BE-AE | -901.66 | -905.45, -897.86 | < .001
BE-BR | -1673.14 | -1676.96, -1669.32 | < .001
BE-AR | -440.88 | -443.83, -437.94 | < .001
AE-BR | -771.48 | -776.16, -766.80 | < .001
AE-AR | 460.77 | 456.79, 464.76 | < .001
BR-AR | 1232.25 | 1228.24, 1236.27 | < .001
Contrasts are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88xFit05oPhLikes: [df5] LikeCount ~ Agency * Phase * Outcome + (1 | Name)
=====================================================================
# Average predicted counts of LikeCount
Agency | Predicted | 95% CI
-------------------------------------
-3 | 1278.44 | 1190.46, 1366.42
-2 | 1229.37 | 1194.91, 1263.83
-1 | 1266.58 | 1255.01, 1278.16
0 | 1398.78 | 1396.15, 1401.40
1 | 1643.43 | 1641.36, 1645.50
2 | 2031.08 | 2027.56, 2034.60
3 | 2610.83 | 2605.81, 2615.85
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
--------------------------------
247.48 | 243.55, 251.40 | < .001
Slopes are presented as counts.
=====================================================================
# (Average) Linear trend for Agency
Slope | 95% CI | p
--------------------------------
247.48 | 243.55, 251.40 | < .001
Slopes are presented as counts.
suppressWarnings(rm(list = ls(pattern = "^gg88")))
gg88 <- ggeff$pred0 %>% plot(limit_range=FALSE, show_data = FALSE, dot_alpha = 0.05)
gg88 <- gg88 + cogsys::theme0 # + timeD + lineE + lineT + lineR + rect3 + scaleA
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.png"), plot = gg88 + cogsys::theme2, width=8, height=24)
ggsave(file = paste0(ggeff$fbasefig, "-pred0-fig.svg"), plot = gg88 + cogsys::theme2, width=16, height=48)
gg88glmmTMB Modelsfile = file.path(
ofd4, "x-summary-tab-model-i0001-base.html")
sjPlot::tab_model(
xFit05aPhLikes,
xFit05oPhLikes,
xFit06aPhRetweets,
show.reflvl = FALSE,
show.intercept = TRUE,
p.style = "numeric_stars",
show.p = FALSE,
## dv.labels = c("Model 1", "Model 2", "Model 3", "Model 4"),
wrap.labels = 125,
file = file)| Like Count | Like Count | Retweet Count | ||||
|---|---|---|---|---|---|---|
| Predictors | Incidence Rate Ratios | CI | Incidence Rate Ratios | CI | Incidence Rate Ratios | CI |
| (Intercept) | 47.38 *** | 41.38 – 54.24 | 29.05 *** | 23.81 – 35.45 | 17.28 *** | 15.28 – 19.55 |
| Agency | 1.05 *** | 1.04 – 1.05 | 0.95 *** | 0.94 – 0.95 | 1.01 *** | 1.01 – 1.02 |
| Phase [AE] | 2.16 *** | 2.15 – 2.16 | 1.58 *** | 1.58 – 1.59 | 1.27 *** | 1.27 – 1.27 |
| Phase [BR] | 2.77 *** | 2.76 – 2.77 | 1.57 *** | 1.57 – 1.58 | 1.85 *** | 1.85 – 1.86 |
| Phase [AR] | 1.92 *** | 1.92 – 1.92 | 1.90 *** | 1.89 – 1.91 | 1.40 *** | 1.40 – 1.40 |
| Agency × Phase [AE] | 1.09 *** | 1.09 – 1.10 | 1.38 *** | 1.37 – 1.38 | 1.18 *** | 1.18 – 1.19 |
| Agency × Phase [BR] | 1.42 *** | 1.41 – 1.42 | 1.32 *** | 1.31 – 1.33 | 1.30 *** | 1.29 – 1.30 |
| Agency × Phase [AR] | 0.68 *** | 0.68 – 0.68 | 0.58 *** | 0.57 – 0.58 | 0.69 *** | 0.69 – 0.69 |
| Outcome [winner] | 2.56 *** | 1.97 – 3.32 | ||||
| Agency × Outcome [winner] | 1.17 *** | 1.16 – 1.17 | ||||
| Phase [AE] × Outcome [winner] | 1.49 *** | 1.48 – 1.49 | ||||
| Phase [BR] × Outcome [winner] | 1.99 *** | 1.99 – 2.00 | ||||
| Phase [AR] × Outcome [winner] | 1.04 *** | 1.04 – 1.04 | ||||
| (Agency × Phase [AE]) × Outcome [winner] | 0.75 *** | 0.74 – 0.75 | ||||
| (Agency × Phase [BR]) × Outcome [winner] | 1.01 ** | 1.00 – 1.01 | ||||
| (Agency × Phase [AR]) × Outcome [winner] | 1.19 *** | 1.19 – 1.20 | ||||
| Zero-Inflated Model | ||||||
| (Intercept) | 0.05 *** | 0.04 – 0.06 | 0.08 *** | 0.07 – 0.09 | 0.08 *** | 0.07 – 0.09 |
| Agency | 0.50 *** | 0.40 – 0.64 | 0.53 *** | 0.40 – 0.72 | 0.61 *** | 0.50 – 0.73 |
| Phase [AE] | 1.02 | 0.85 – 1.23 | 1.56 *** | 1.26 – 1.93 | 1.33 *** | 1.16 – 1.54 |
| Phase [BR] | 0.77 ** | 0.64 – 0.92 | 1.13 | 0.91 – 1.40 | 1.14 | 0.99 – 1.32 |
| Phase [AR] | 0.69 *** | 0.57 – 0.85 | 1.22 | 0.96 – 1.54 | 0.98 | 0.84 – 1.15 |
| Agency × Phase [AE] | 0.66 * | 0.47 – 0.93 | 0.98 | 0.65 – 1.47 | 0.49 *** | 0.37 – 0.63 |
| Agency × Phase [BR] | 0.78 | 0.56 – 1.10 | 1.27 | 0.85 – 1.90 | 0.67 ** | 0.52 – 0.87 |
| Agency × Phase [AR] | 0.65 * | 0.45 – 0.95 | 1.10 | 0.70 – 1.74 | 0.58 *** | 0.44 – 0.77 |
| Outcome [winner] | 0.33 *** | 0.25 – 0.44 | ||||
| Agency × Outcome [winner] | 1.07 | 0.64 – 1.78 | ||||
| Phase [AE] × Outcome [winner] | 0.40 *** | 0.26 – 0.61 | ||||
| Phase [BR] × Outcome [winner] | 0.25 *** | 0.15 – 0.41 | ||||
| Phase [AR] × Outcome [winner] | 0.28 *** | 0.17 – 0.45 | ||||
| (Agency × Phase [AE]) × Outcome [winner] | 0.84 | 0.39 – 1.79 | ||||
| (Agency × Phase [BR]) × Outcome [winner] | 0.69 | 0.29 – 1.65 | ||||
| (Agency × Phase [AR]) × Outcome [winner] | 1.17 | 0.50 – 2.76 | ||||
| Random Effects | ||||||
| σ2 | 0.00 | 0.00 | 0.00 | |||
| τ00 | 3.97 Name | 3.61 Name | 3.23 Name | |||
| ICC | 1.00 | 1.00 | 1.00 | |||
| N | 845 Name | 845 Name | 845 Name | |||
| Observations | 60000 | 60000 | 60000 | |||
| Marginal R2 / Conditional R2 | 0.048 / 1.000 | 0.132 / 1.000 | 0.026 / 1.000 | |||
| * p<0.05 ** p<0.01 *** p<0.001 | ||||||
./data/20240428T200156-politicians-aux-analysis/n0001-init//n0001-models-phase-i0021-all/x-summary-tab-model-i0001-base.html
xFit21aPhOutcome
model <- "xFit21aPhOutcome"
suppressWarnings(rm(list = model))
assign(
model,
glmmTMB::glmmTMB(
formula = Outcome ~ Agency + (1 | Name),
family = binomial(link = "logit"),
data = df2 %>%
filter(Phase=="BE") %>%
## slice_sample(n=2e6) %>%
mutate(Outcome=as.integer(Outcome)-1) %>%
identity()
))
fbase <- get_model_info(model, ofd4)xFit21aPhOutcome: [%>%] [df2 %>% filter(Phase == "BE") %>% mutate(Outcome = as.integer(Outcome) - 1)] [identity()] Outcome ~ Agency + (1 | Name)
Family: binomial ( logit )
Formula: Outcome ~ Agency + (1 | Name)
Data: df2 %>% filter(Phase == "BE") %>% mutate(Outcome = as.integer(Outcome) -
1) %>% identity()
AIC BIC logLik deviance df.resid
1346.4 1378.2 -670.2 1340.4 304617
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Name (Intercept) 13586 116.6
Number of obs: 304620, groups: Name, 850
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 18.8140 0.7048 26.693 <2e-16 ***
Agency 0.1325 0.8099 0.164 0.87
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---------------------------------------------------------------------
xFit21aPhOutcome: [%>%] [df2 %>% filter(Phase == "BE") %>% mutate(Outcome = as.integer(Outcome) - 1)] [identity()] Outcome ~ Agency + (1 | Name)
# R2 for Mixed Models
Conditional R2: 1.000
Marginal R2: 0.000
---------------------------------------------------------------------
xFit21aPhOutcome: [%>%] [df2 %>% filter(Phase == "BE") %>% mutate(Outcome = as.integer(Outcome) - 1)] [identity()] Outcome ~ Agency + (1 | Name)
# Intraclass Correlation Coefficient
Adjusted ICC: 1.000
Unadjusted ICC: 1.000
---------------------------------------------------------------------
xFit21aPhOutcome: [%>%] [df2 %>% filter(Phase == "BE") %>% mutate(Outcome = as.integer(Outcome) - 1)] [identity()] Outcome ~ Agency + (1 | Name)
# ICC by Group
Group | ICC
-------------
Name | 1.000
---------------------------------------------------------------------
=====================================================================
df0
df2
df3
df5
df8
=====================================================================
fit01aPh
fit02aPh
fit03aPh
fit04aPh
fit04xPh
xFit05aPhLikes
xFit05oPhLikes
xFit06aPhRetweets
xFit21aPhOutcome